Change Management: Big data is one thing; decision support processes are another

CIOs must realize that innovation needs to go well beyond the technology used to manage big data, according to Gartner, Inc. To get maximum value, enterprises will need to seek and embrace innovation in the way business problems are analyzed with big data.

"Big data requires an enterprise to embrace innovation on two levels," says Hung LeHong, research vice president at Gartner. "First, the technology itself is innovative. Second, enterprises must be willing to innovate in the way they perform decision support and analytics. This second reason is not a technology challenge, but rather a process and change management challenge.

"Big data technologies bring innovative ways of analyzing existing business problems and opportunities. New data sources and new analytics can improve the enterprise in ways that have never been used before."

Big data's ability to analyze unstructured data in large volumes and from disparate sources leads to innovative opportunities. In most cases, there has been very little precedence for the ways big data can add value to an enterprise. It was never possible to run these kinds of analyses or access these new types of data. Seeking value from big data technologies requires innovative thinking and a willingness to accept and trust these sources and methods. CIOs should treat big data projects as innovation projects that will require change management efforts. The business will need time to trust new data sources and new analytics and enterprises should start small with pilots that allow full transparency on the data, the analytics and the resulting insight.

However, big data isn't just about the large sources of external data, such as public social network data. Creative CIO thinking can unearth valuable information sources already inside the enterprise that are underused.

"Perhaps CIOs feel more comfortable starting with internal data sources, because the thinking is that much of it is already being managed by IT," says LeHong. "However, in many cases, these internal data sources are not controlled by IT at all. For example, call center recordings, security camera footage, and operational data from manufacturing equipment all represent potential internal sources of data to investigate, but they are usually not under the control of IT."

Therefore, CIOs and their teams will need to work with the business to fully understand the pockets of data that are available. With some creative thinking, even data that is already captured can be made richer. Enterprises that use big data technologies can afford to keep the full, raw data, building up rich sources of data that can provide new insight. However, CIOs will need to ensure that there is always a clear business purpose and outcome for storing this new data.

Internal data has an additional advantage. It is a good starting point for big data projects because the enterprise already owns the data, and it may be easier and/or less costly than accessing external data sources. Also, compared with external sources, the enterprise will be more likely to trust the internal data because it comes from its own systems, logs, and other assets.

Some enterprises have used big data technologies to make existing analytics faster. Although technology may enable faster speed, getting business value from that speed often requires process changes.

Gartner research shows that early adopter enterprises that implemented faster analytical capabilities changed their processes to get the maximum benefit from the speed. For some enterprises, the speed in analysis provides the ability to include a full week of sales data when running analytics, such as price/promotion/markdown optimization. In the past, because these optimizations would take a day to run, Sunday's sales data often did not make it into the calculations. Now, with the ability to run the optimizations in minutes, enterprises can include the full week's data-making their optimizations immediately up to date with market activity.

"CIOs must ensure that big data projects that improve analytical speed always include a process redesign effort that aims at getting maximum benefit from that speed," says LeHong. "Before pursuing big data investments, ensure that the evaluating team has a clear understanding of how faster analytics will lead to an improved business outcome-and build this into the business case."

John Ginovsky is a contributing editor of Banking Exchange and editor of the publication’s Tech Exchange e-newsletter. For more than two decades he’s written about the commercial banking industry, specializing in its technological side and how it relates to the actual business of banking. In addition to his weekly blogs—"Making Sense of It All"—he contributes fresh, original stories to each Tech Exchange issue based on personal interviews or exclusive contributed pieces. He previously was senior editor for Community Banker magazine (which merged into ABA Banking Journal) and for ABA Banking Journal and was managing editor and staff reporter for ABA’s Bankers News. Email him at [email protected]